Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach.

Neurons are often classified by their morphological and molecular properties. The online knowledge base Hippocampome.org primarily defines neuron types from the rodent hippocampal formation based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites. For each neuron type, this open-access resource reports any and all published information regarding the presence or absence of known molecular markers, including calcium-binding proteins, neuropeptides, receptors, channels, transcription factors, and other molecules of biomedical relevance. The resulting chemical profile is relatively sparse: even for the best studied neuron types, the expression or lack thereof of fewer than 70 molecules has been firmly established to date. The mouse genome-wide in situ hybridization mapping of the Allen Brain Atlas provides a wealth of data that, when appropriately analyzed, can substantially augment the molecular marker knowledge in Hippocampome.org. Here we focus on the principal cell layers of dentate gyrus (DG), CA3, CA2, and CA1, which together contain approximately 90% of hippocampal neurons. These four anatomical parcels are densely packed with somata of mostly excitatory projection neurons. Thus, gene expression data for those layers can be justifiably linked to the respective principal neuron types: granule cells in DG and pyramidal cells in CA3, CA2, and CA1. In order to enable consistent interpretation across genes and regions, we screened the whole-genome dataset against known molecular markers of those neuron types. The resulting threshold values allow over 6000 very-high confidence (>99.5%) expressed/not-expressed assignments, expanding the biochemical information content of Hippocampome.org more than five-fold. Many of these newly identified molecular markers are potential pharmacological targets for major neurological and psychiatric conditions. Furthermore, our approach yields reasonable expression/non-expression estimates for every single gene in each of these four neuron types with >90% average confidence, providing a considerably complete genetic characterization of hippocampal principal neurons.

Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties.

Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. Hippocampome.org is a web-accessible neuroinformatics resource that organizes existing data about essential properties of all known neuron types in the rodent hippocampal formation. Hippocampome.org links evidence supporting the assignment of a property to a type with direct pointers to quotes and figures. Mining this knowledge from peer-reviewed reports reveals the troubling extent of terminological ambiguity and undefined terms. Examples span simple cases of using multiple synonyms and acronyms for the same molecular biomarkers (or other property) to more complex cases of neuronal naming. New publications often use different terms without mapping them to previous terms. As a result, neurons of the same type are assigned disparate names, while neurons of different types are bestowed the same name. Furthermore, non-unique properties are frequently used as names, and several neuron types are not named at all. In order to alleviate this nomenclature confusion regarding hippocampal neuron types and properties, we introduce a new functionality of Hippocampome.org: a fully searchable, curated catalog of human and machine-readable definitions, each linked to the corresponding neuron and property terms. Furthermore, we extend our robust approach to providing each neuron type with an informative name and unique identifier by mapping all encountered synonyms and homonyms.

Non-homogeneous stereological properties of the rat hippocampus from high-resolution 3D serial reconstruction of thin histological sections.

Integrating hippocampal anatomy from neuronal dendrites to whole system may help elucidate its relation to function. Toward this aim, we digitally traced the cytoarchitectonic boundaries of the dentate gyrus (DG) and areas CA3/CA1 throughout their entire longitudinal extent from high-resolution images of thin cryostatic sections of adult rat brain. The 3D computational reconstruction identified all isotropic 16 µm voxels with appropriate subregions and layers (http://krasnow1.gmu.edu/cn3/hippocampus3d). Overall, DG, CA3, and CA1 occupied comparable volumes (15.3, 12.2, and 18.8 mm(3), respectively), but displayed substantial rostrocaudal volumetric gradients: CA1 made up more than half of the posterior hippocampus, whereas CA3 and DG were more prominent in the anterior regions. The CA3/CA1 ratio increased from ∼0.4 to ∼1 septo-temporally because of a specific change in stratum radiatum volume. Next we virtually embedded 1.8 million neuronal morphologies stochastically resampled from 244 digital reconstructions, emulating the dense packing of granular and pyramidal layers, and appropriately orienting the principal dendritic axes relative to local curvature. The resulting neuropil occupancy reproduced recent electron microscopy data measured in a restricted location. Extension of this analysis across each layer and subregion over the whole hippocampus revealed highly non-homogeneous dendritic density. In CA1, dendritic occupancy was >60% higher temporally than septally (0.46 vs. 0.28, s.e.m. ∼0.05). CA3 values varied both across subfields (from 0.35 in CA3b/CA3c to 0.50 in CA3a) and layers (0.48, 0.34, and 0.27 in oriens, radiatum, and lacunosum-moleculare, respectively). Dendritic occupancy was substantially lower in DG, especially in the supra-pyramidal blade (0.18). The computed probability of dendrodendritic collision significantly correlated with expression of the membrane repulsion signal Down syndrome cell adhesion molecule (DSCAM). These heterogeneous stereological properties reflect and complement the non-uniform molecular composition, circuit connectivity, and computational function of the hippocampus across its transverse, longitudinal, and laminar organization.

The membrane response of hippocampal CA3b pyramidal neurons near rest: Heterogeneity of passive properties and the contribution of hyperpolarization-activated currents.

Pyramidal neurons in the CA3 region of the hippocampal formation integrate synaptic information arriving in the dendrites within discrete laminar regions. At potentials near or below the resting potential integration of synaptic signals is most affected by the passive properties of the cell and hyperpolarization-activated currents (I(h)). Here we focused specifically on a subset of neurons within the CA3b subregion of the rat hippocampus in order to better understand their membrane response within subthreshold voltage ranges. Using a combined experimental and computational approach we found that the passive properties of these neurons varied up to fivefold between cells. Likewise, there was a large variance in the expression of I(h) channels. However, the contribution of I(h) was minimal at resting potentials endowing the membrane with an apparent linear response to somatic current injection within +/-10 mV. Unlike in CA1 pyramidal neurons, however, I(h) activation was not potentiated in an activity-dependent manner. Computer modeling, based on a combination of voltage- and current-clamp data, suggested that an increasing density of these channels with distance from the soma, compared with a uniform distribution, would have no significant effect on the general properties of the cell because of their relatively lower expression. Nonetheless, temporal summation of excitatory inputs was affected by the presence of I(h) in the dendrites in a frequency- and distance-dependent fashion.

Computer generation and quantitative morphometric analysis of virtual neurons.

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

Generation, description and storage of dendritic morphology data.

It is generally assumed that the variability of neuronal morphology has an important effect on both the connectivity and the activity of the nervous system, but this effect has not been thoroughly investigated. Neuroanatomical archives represent a crucial tool to explore structure-function relationships in the brain. We are developing computational tools to describe, generate, store and render large sets of three-dimensional neuronal structures in a format that is compact, quantitative, accurate and readily accessible to the neuroscientist. Single-cell neuroanatomy can be characterized quantitatively at several levels. In computer-aided neuronal tracing files, a dendritic tree is described as a series of cylinders, each represented by diameter, spatial coordinates and the connectivity to other cylinders in the tree. This 'Cartesian' description constitutes a completely accurate mapping of dendritic morphology but it bears little intuitive information for the neuroscientist. In contrast, a classical neuroanatomical analysis characterizes neuronal dendrites on the basis of the statistical distributions of morphological parameters, e.g. maximum branching order or bifurcation asymmetry. This description is intuitively more accessible, but it only yields information on the collective anatomy of a group of dendrites, i.e. it is not complete enough to provide a precise 'blueprint' of the original data. We are adopting a third, intermediate level of description, which consists of the algorithmic generation of neuronal structures within a certain morphological class based on a set of 'fundamental', measured parameters. This description is as intuitive as a classical neuroanatomical analysis (parameters have an intuitive interpretation), and as complete as a Cartesian file (the algorithms generate and display complete neurons). The advantages of the algorithmic description of neuronal structure are immense. If an algorithm can measure the values of a handful of parameters from an experimental database and generate virtual neurons whose anatomy is statistically indistinguishable from that of their real counterparts, a great deal of data compression and amplification can be achieved. Data compression results from the quantitative and complete description of thousands of neurons with a handful of statistical distributions of parameters. Data amplification is possible because, from a set of experimental neurons, many more virtual analogues can be generated. This approach could allow one, in principle, to create and store a neuroanatomical database containing data for an entire human brain in a personal computer. We are using two programs, L-NEURON and ARBORVITAE, to investigate systematically the potential of several different algorithms for the generation of virtual neurons. Using these programs, we have generated anatomically plausible virtual neurons for several morphological classes, including guinea pig cerebellar Purkinje cells and cat spinal cord motor neurons. These virtual neurons are stored in an online electronic archive of dendritic morphology. This process highlights the potential and the limitations of the 'computational neuroanatomy' strategy for neuroscience databases.

Structural dependence of the cellular isoform of prion protein on solvent: spectroscopic characterization of an intermediate conformation.

Using circular dichroism, fluorescence, and infrared spectroscopies, we studied the secondary structure of purified hamster PrP(C) in the presence of the mild, nonionic detergent octylglucoside. Under these native conditions, PrP(C) displayed an unexpectedly high beta-sheet component, intermediate between the values previously reported for PrP(Sc) and an isoform of PrP(C) isolated in a zwitterionic detergent. The structure of PrP(C) appeared to depend strongly on the detergent and/or phase. Switching from octylglucoside to zwitterion 3-14 drastically modified PrP secondary structure by increasing the alpha-helix while abolishing the beta-sheet component. In contrast, the conformation of PrP(C) in zwitterion was highly stable, since reverting to octylglucoside did not restore the original native structure. These and other results show that native PrP(C) in octylglucoside has some of the conformational characteristics that make the protein susceptible to conversion into PrP(Sc). Most importantly, this is the first study to demonstrate the intrinsic plasticity of the full-length native PrP(C) isolated from animal brains.

Site I on human albumin: differences in the binding of (R)- and (S)-warfarin.

The binding of drugs known to interact with area I on human serum albumin (HSA) was investigated using a chiral stationary phase obtained by anchoring HSA to a silica matrix. In particular, this high-pressure affinity chromatography selector was employed to study the binding properties of the individual enantiomers of warfarin. The pH and composition of the mobile phase modulate the enantioselective binding of warfarin. Displacement chromatography experiments evidenced significant differences in the binding of the warfarin enantiomers to site I. The (S)-enantiomer was shown to be a direct competitor for (R)-warfarin, while (R)-warfarin was an indirect competitor for the (S)-enantiomer. Salicylate directly competed with (R)-warfarin and indirectly with (S)-warfarin. This behavior was confirmed by difference CD experiments, carried out with the same [HSA]/[drug] system in solution.

Progress and perspectives in computational neuroanatomy.

The tremendous increase in processing power of personal computers has recently allowed the construction of highly sophisticated models of neuronal function and behavior. Anatomy plays a fundamental role in supporting and shaping nervous system activity, yet to date most details of such a role have escaped the efforts of experimental and theoretical neuroscientists, mainly because of the problem's complexity. When accurate cellular morphologies are included in electrophysiological computer simulations, quantitative and qualitative effects of dendritic structure on firing properties can be extensively characterized. Complete models of dendritic morphology can be implemented to allow the computer generation of virtual neurons that model the anatomical characteristics of their real counterparts to a great degree of approximation. From a restricted and already available experimental database, stochastic and statistical algorithms can create an unlimited number of non-identical virtual neurons within several mammalian morphological classes, storing them in a compact and parsimonious format. When modeled neurons are distributed in three-dimensional and biologically plausible rules governing axonal navigation and connectivity are added to the simulations, entire portions of the nervous system can be "grown" as anatomically realistic neural networks. These computational constructs are useful to determine the influence of local geometry on system neuroanatomy, and to investigate systematically the mutual interactions between anatomical parameters and electrophysiological activity at the network level. A detailed computer model of a "virtual brain" that was truly equivalent to the biological structure could in principle allow scientists to carry out experiments that could not be performed on real nervous systems because of physical constraints. The computational approach to neuroanatomy is just at its beginning, but has a great potential to enhance the intuition of investigators and to aid neuroscience education. Anat Rec (New Anat): 257:195-207, 1999.

Use of CD and FT-IR to determine the secondary structure of purified proteins in the low-microgram range.

The spectroscopic characterization of protein secondary structure is often partially unreliable when samples are not extremely pure and abundant. This problem may be overcome by the combination of circular dichroism (CD) and Fourier transform infrared spectroscopy (FT-IR). We used these methods to characterize the secondary structure of two proteins of neurobiological interest, calexcitin (CE) and the cellular isoform of prion protein (PrPC). Both proteins were purified with multiple chromatographic steps and were obtained in buffer with high purity (> 95%) and in low amount (approximately 2 micrograms). The samples were analyzed by circular dichroism (down to 184 or 182 nm), recovered, and deposited on films for infrared analysis. The spectral deconvolution from the two methods yielded secondary structures in good agreement with each other as well as with theoretical predictions based on amino acid sequence. The conformation of CE was found to be dependent on its concentration and on calcium binding. The secondary structure of cellular native PrP varied dramatically with the detergent used. In conclusion, the combination of CD and FT-IR analysis is suitable for the characterization of the conformational changes induced by ligand binding and/or by different solvent conditions when the protein of interest is only scarcely available. The methods used here provide valuable insights into the putative correlation between protein structure and activity.

Ligand binding to a human serum albumin stationary phase: use of same-drug competition to discriminate pharmacologically relevant interactions.

A technique based on a human serum albumin (HSA) stationary phase high-pressure liquid chromatography (HPLC) has been successfully used for the past few years to characterize the interactions between HSA and new substrates. Immobilized HSA conserves the binding properties of the protein in solution, allowing fast and reliable analyses of binding interactions. Nevertheless, clear evidence that all binding mechanisms of HSA-HPLC are pharmacologically relevant is so far lacking. In particular, non-stoichiometric interactions of injected ligands with stationary phase components such as silica and the amino acid medium (other than protein binding areas) might interfere with the correlation between chromatographic retention and HSA binding. Here we present a quantitative method to distinguish between the molecular interactions of a ligand with binding areas of potential pharmacological interest and other, non-saturable binding mechanisms. Such a method, based on HPLC same-ligand displacement, is simple and reliable, as confirmed by in situ protein denaturation. Consequently, we were able to distinguish between different types of competitions detected in the co-binding of two drugs to HSA.

Secondary structure and Ca2+-induced conformational change of calexcitin, a learning-associated protein.

Calexcitin/cp20 is a low molecular weight GTP- and Ca2+-binding protein, which is phosphorylated by protein kinase C during associative learning, and reproduces many of the cellular effects of learning, such as the reduction of potassium currents in neurons. Here, the secondary structure of cloned squid calexcitin was determined by circular dichroism in aqueous solution and by Fourier transform infrared spectroscopy both in solution and on dried films. The results obtained with the two techniques are in agreement with each other and coincide with the secondary structure computed from the amino acid sequence. In solution, calexcitin is one-third in alpha-helix and one-fifth in beta-sheet. The conformation of the protein in solid state depends on the concentration of the starting solution, suggesting the occurrence of surface aggregation. The secondary structure also depends on the binding of calcium, which causes an increase in alpha-helix and a decrease in beta-sheet, as estimated by circular dichroism. The conformation of calexcitin is independent of ionic strength, and the calcium-induced structural transition is slightly inhibited by Mg2+ and low pH, while favored by high pH. The switch of calexcitin's secondary structure upon calcium binding, which was confirmed by intrinsic fluorescence spectroscopy and nondenaturing gel electrophoresis, is reversible and occurs in a physiologically meaningful range of Ca2+ concentration. The calcium-bound form is more globular than the apoprotein. Unlike other EF-hand proteins, calexcitin's overall lipophilicity is not affected by calcium binding, as assessed by hydrophobic liquid chromatography. Preliminary results from patch-clamp experiments indicated that calcium is necessary for calexcitin to inhibit potassium channels and thus to increase membrane excitability. Therefore the calcium-dependent conformational equilibrium of calexcitin could serve as a molecular switch for the short term modulation of neuronal activity following associative conditioning.